LLMTraining & AlignmentUpdated 2026.04.28

Fine-tuning

Also known as미세조정추가 학습Instruction Tuning

In one line

Fine-tuning takes an already pretrained LLM and trains it further on a narrower dataset to specialise it for a domain, task or voice — the most common path for adapting an LLM to your own data.

Going deeper

Fine-tuning takes a pretrained LLM and trains it further on a smaller, task-specific dataset — say, your customer support transcripts or your brand voice. If pretraining is the general-education degree, fine-tuning is the on-the-job training.

The marketing appeal is obvious: 'make the AI sound like us, with our answers'. The trade-off is cost and operational overhead, which is why many teams now prefer to leave the model alone and inject knowledge via RAG instead.

Honestly, in most marketing use cases you can get by without fine-tuning. A solid system prompt, a few-shot examples and RAG cover the bulk of it. Fine-tuning earns its keep when you have plenty of labelled data and need very consistent, narrow outputs.

Related terms

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